Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis

Objective: Postpartum depression is a frequent complication after childbirth, affecting maternal health, infant development, and family well-being. This study evaluated the role of artificial intelligence (AI) in preventing and detecting postpartum depression early. Methods: A systematic search was conducted in Scopus, PubMed, Web of Science, and CINAHL for studies (2020-2025) applying AI to identify postpartum depression. PRISMA guidelines guided selection and appraisal. Two random-effects meta-analyses estimated pooled sensitivity and accuracy based on total sample size and reported metrics. Results: Of 1,857 records, 16 studies met inclusion criteria. Machine learning models (Random Forest, XGBoost, neural networks) showed greater accuracy than traditional methods. Integration of AI with medical records and social media data enabled earlier, personalized detection. Reported challenges included algorithmic bias, data privacy, and implementation barriers. Pooled sensitivity was 69% (95% CI: 55-81%; n=277,496) and accuracy 79% (95% CI: 73-85%; n=306,156). Conclusions: AI shows promise for enhancing postpartum depression detection and prevention but requires addressing ethical, technical, and educational challenges to achieve equitable clinical integration. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251004175, identifier CRD420251004175.